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Friday, January 20, 2017

What did Big Data find when it analyzed 150 years of British history?

I have always respected mega data approaches for their ability to determine accurate time frames from which to conduct analysis and to uncover not so obvious aspects that could be easily overlooked.

This work has the advantage of been about as good as it gets for the last two centuries.

I would like to see the same done for American newspapers in order to uncover fashions of fake news stories as well. The nineteenth century was terrible.

What did Big Data find when it analyzed 150 years of British history?

January 9, 2017

http://phys.org/news/2017-01-big-years-british-history.html

What could be learnt about the world if you
could read the news from over 100 local newspapers for a period of 150
years? This is what a team of Artificial Intelligence (AI) researchers
from the University of Bristol have done, together with a social
scientist and a historian, who had access to 150 years of British
regional newspapers.

The patterns
that emerged from the automated analysis of 35 million articles ranged
from the detection of major events, to the subtle variations in gender bias
across the decades. The study has investigated transitions such as the
uptake of new technologies and even new political ideas, in a new way
that is more like genomic studies than traditional historical
investigation.

The team of academics, led by Professor Nello Cristianini,
collaborated closely with the company findmypast, who is digitising
historical newspapers from the British Library as part of their British
Newspaper Archive project.

The main focus of the study was to establish if major historical and
cultural changes could be detected from the subtle statistical
footprints left in the collective content of local newspapers.
How many women were mentioned? In which year did electricity start
being mentioned more than steam? Crucially, this work goes well beyond
counting words, and deploys AI methods to identify people and their
gender, or locations and their position on the map.

The landmark study, part of the University of Bristol's ThinkBIG
project, collected a huge amount of regional newspapers from the UK,
including geographical and time-based information that is not available
in other textual data such as books. Over 35 million articles and 28.6
billion words, from the British Library's newspaper collections,
representing 14 per cent of all British regional outlets from 1800 to
1950, were used for the study.

Nello Cristianini, Professor of Artificial Intelligence, from the
Department of Engineering Mathematics, said: "The key aim of the study
was to demonstrate an approach to understanding continuity and change in
history, based on the distant reading of a vast body of news, which
complements what is traditionally done by historians.

"The research team showed that changes and continuities detected in
newspaper content can reflect culture, biases in representation or
actual real-world events. More detailed studies on the same data will be
performed."

Simple content analysis allowed the researchers to detect specific
key events like wars, epidemics, coronations or gatherings with high
accuracy, while the use of more refined techniques from AI enabled the
research team to move beyond counting words by detecting references to
named entities, such as individuals, companies and locations.

Some of the results were to be expected, and acted as a rational
check for the approach, while other outcomes were not so obvious at the
start of the analysis.The researchers found in the areas of values,
beliefs and UK politics that in the 19th century Gladstone was much more
newsworthy than Disraeli; until the 1930's Liberals were mentioned more
than Conservatives, and that reference to British identity took off in
the 20th century.

In the subjects of technology and economy, the research team tracked
the steady decline of steam and the rise of electricity, with a crossing
point of 1898; trains overtook horses in popularity in 1902; and the
four largest peaks for 'panic' corresponded with negative market
movements linked to banking crises in 1826, 1847, 1857 and 1866.

The researchers have shown in the subjects of social change and
popular culture that the Suffragette movement fell within a delimited
time interval 1906 to 1918; 'actors', 'singers' and 'dancers' began to
increase in the 1890s, rising significantly from then on, while
references to 'politicians', by contrast, gradually declined from the
early 20th century; and that 'football' was more prominent than
'cricket' from 1909.

Replicating a previous study done on book content, the researchers
then moved on to link famous people in the news to their profession,
finding that politicians and writers are most likely to achieve
notoriety within their lifetimes, while scientists and mathematicians
are less likely to achieve fame but decline less sharply.

More importantly, the researchers found that males are systematically
more present than females during the entire period studied, but there
is a slow increase of the presence of women after 1900, although it is
difficult to attribute this to a single factor at the time.
Interestingly, the amount of gender bias in the news over the period of
investigation is not very different from current levels.

Dr Tom Lansdall-Welfare, Research Associate in Machine Learning in
the Department of Computer Science, who led the computational part of
the study, said: "We have demonstrated that computational approaches can
establish meaningful relationships between a given signal in
large-scale textual corpora and verifiable historical moments.

"However, what cannot be automated is the understanding of the
implications of these findings for people, and that will always be the
realm of the humanities and social sciences, and never that of
machines."

The researchers believe that these data-driven approaches can
complement the traditional method of close reading in detecting trends
of continuity and change in historical corpora.

'Content analysis of 150 years of British periodicals' by Lansdall-Welfare et al is published in PNAS.

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18 years old, having cleaned out my HS library, I concluded the only ambition worth having was becoming a great genius. An inner voice cheered. Yet it is my path I have shared much to the Human Gesalt. Mar 2017 - 4.56 Mil Pg Views, March 2013 - Posted my paper introducing CLOUD COSMOLOGY & NEUTRAL NEUTRINO described as the SPACE TIME PENDULUM. Sep 2010 -My essay titled A NEW METRIC WITH APPLICATIONS TO PHYSICS AND SOLVING CERTAIN HIGHER ORDERED DIFFERENTIAL EQUATIONS has been published in Physics Essays(AIP) June 2010 quarterly. 40 years ago I took an honors degree in applied mathematics from the University of Waterloo. My interest was Relativity and my last year there saw me complete a 900 level course under Hanno Rund on his work in Relativity. I continued researching new ideas and knowledge since that time and I have prepared a book for publication titled Paradigms Shift. I maintain my blog as a day book and research tool to retain data, record impressions, interpretations and to introduce new insights to readers.